A typical inertial measurement unit (IMU) consists of a three-axis gyroscope and a three-axis accelerometer, which measure rotational velocity and gravity-compensated linear acceleration (i.e. specific force) of a moving platform respectively.
As the IMU captures motion information without relying on any external infrastructure, it has been extensively used in navigation systems and applications, e.g., mobile devices [1]}, [2]}, ground robots [3]}, miniature drones [4]}, autonomous vehicles [5]}, and so on.
Up to the hardware design and manufacturing process, the price of an IMU varies can vary over a wide range, e.g. 1-10k dollars [6]}. In this work, we focus on the widely-used low-cost IMUs, whose data quality is much lower than that of the high-end IMUs. This makes algorithms and systems of either improving the IMU data quality or enhancing the data processing pipeline of noteworthy significance in the research community.
